Imitation of Life

The Engine Room

In trying to get a sense of how the WholeCell simulation works, I chose three modules for close examination. They are the modules for metabolism, for the transcription of genetic information and for the size and shape of the growing cell.

The metabolism module is where most of the classical biochemistry happens. Here we are in the blue-collar sector of the cell’s economy, dealing with energy production, manufacturing and the handling of raw materials and wastes. (Most of the other modules are more concerned with white-collar chores of information processing.)

Even in a cell as tiny as M. genitalium, metabolism involves a bewildering maze of interlinked chemical processes. The metabolism module of the WholeCell model includes 104 enzymes, 585 substrates, 441 chemical reactions and 204 transport processes.

The size and complexity of this network foils many conventional methods of analysis. The rate of any given chemical reaction depends in part on the concentrations of the reactants and the products. But the products of one reaction are the inputs to another, so all the processes are closely coupled and cannot be solved independently. An added complication is that biological networks include cycles, such as the citric acid cycle of carbohydrate metabolism. With a cycle, the products of a given reaction may go all the way around the loop and reappear as inputs to the same reaction, so that the overall flux of material through the network is not uniquely defined.

To sidestep these difficulties, the WholeCell metabolic module relies on a methodology called flux-balance analysis. The underlying idea is that even if a reaction network does not have a unique solution, it may well have a best solution. The process for finding that solution is much like the algorithm used to optimize the operations of a chemical plant or an oil refinery. Suppose a refinery has a range of products (gasoline, diesel fuel and so on), which differ in manufacturing cost and market value. The mathematical technique of linear programming computes a mix of products that maximizes profit. Applying the same method to the living cell yields a set of reaction rates that make the most efficient use of available resources, such as nutrients. (It’s not known with certainty that microorganisms optimize their growth in this way, but it’s a plausible assumption in the context of Darwinian natural selection.)